Artificial Intelligence is a computer science family in which systems are built to perform functions that frequently demand human intelligence. Tasks involve learning through experience, recognising patterns, interpreting natural language, and exercising judgments. AI encompasses numerous technologies, including machine learning. In this, algorithms learn to improve themselves by experience with the input at hand, while deep learning mimics the networks of the human brain, possibly for processing complex patterns.
Introduction to Data Science
You will learn the basic concepts of Data Science: its lifecycle and integration with AI, how AI is used, and how Python fits into machine learning. At FITA Academy, a learning relationship exists between Python and AI, which is the prerequisite for learning the Artificial Intelligence Syllabus.
Introduction to Python
In this section, you’ll learn about Python’s history and evolution, including the differences between Python 2 and 3. You’ll also get hands-on experience installing Python, setting up your environment, and understanding Python identifiers, keywords, and indentation. This module covers comments, command line arguments, user input, and basic data types and variables, providing a solid foundation for Python programming.
List, Ranges & Tuples in Python
In this module, you’ll learn how to understand and use lists, iterators, and ranges in Python. To manage data efficiently, you’ll also explore generators, comprehensions, and lambda expressions.
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Python Dictionaries and Sets
You will delve into Python dictionaries, including usage and features, with many examples. You will also learn about sets, including properties and applications. In the AI Syllabus at FITA Academy, this module is part of our curriculum for enhancing the user’s Python skills. The AI Syllabus incorporates these critical components to provide a robust background in AI Python programming applications.
Input and Output in Python
This module teaches how to handle files efficiently. We’ll cover reading and writing text files, appending data, and managing binary files manually and with the Pickle module.
- Reading and writing text files
- Appending to Files
- Writing Binary Files Manually and using Pickle Module
Python Functions
In this module, you’ll learn to define and use Python functions, including creating your user-defined functions. Explore Python packages and their functions, understand anonymous functions, and master loops and statements. You will also be introduced to Python modules and packages, making your coding more efficient and organised.
Python Exceptions Handling
In the “Python Exceptions Handling” module of our Artificial Intelligence Course Syllabus at FITA Academy, you will discover how to deal with errors in Python elegantly. You’ll explore what exceptions are, how to manage them with try-except blocks, the try-finally clause, standard exceptions, raising exceptions, and creating user-defined exceptions.
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Python Regular Expressions
In this section, you’ll learn the basics of regular expressions, including what they are and how to use the match and search functions. You’ll also explore matching vs searching, search and replace techniques, and extended regex with wildcards.
Useful Additions
You learn all the powerful features of Python under the “Useful Additions” module, including debugging techniques, breakpoints, and using an IDE in the best way possible to facilitate the coding process. We will describe these notions simply and in practice at FITA Academy.
Understanding Data: A Comprehensive Guide
Introduction to Data Understanding
In the “Introduction to Data Understanding” module, you’ll learn the importance of grasping data, its crucial role in decision-making, and how data forms the foundation of practical analysis. These concepts are integral to the Artificial Intelligence Course Syllabus at FITA Academy.
- Importance of Data Understanding
- Role in Decision Making
- Data as the Foundation of Analysis
Types of Data
You’ll learn about Structured Data and Tabular Data, including CSV (Comma-Separated Values) Files and Relational Databases, focusing on effectively managing and analysing these data formats.
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Unstructured Data
Understand how to process and analyse Textual Data, Images, Audio and Speech Data, Video Data, and key sub-modules.
Semi-Structured Data
In this lesson, you will learn about Semi-Structured Data. Specifically, you’ll start with JSON (JavaScript Object Notation) and XML (eXtensible Markup Language). You’ll explore their format, structure and critical applications.
Exploring Tabular Data
In this section, you will learn rows, including columns, data types and formats, and descriptive statistics to understand tabular data appropriately.
CSV Files: Structure and Usage
This module covers the basics of a CSV file, its advantages and limitations, and how to read and write one from a programming perspective. These topics appear in the AI and ML syllabus.
Tools and Techniques for Data Understanding
In this module, you will learn essential tools and techniques for understanding data. You will also learn about data visualisation with charts, graphs, heatmaps, and scatter plots so you can dig deeper into the data to get meaningful insights.
Difficulties in Understanding of Data
In this module you will learn how to handle missing, noisy, and inconsistent data. You will also see practical techniques for data cleaning and refinement, thereby making better analyses and decisions.
Data Manipulation using Python
You would learn about manipulating data using Python discussing, extraction, cleaning, transformation, integration, and documentation. This module ensures that the concepts related to management and analysis techniques are well understood. It is crucial for the AI Course Syllabus and gives an excellent basis for further applications of AI.
- Understanding different types of Data
- Understanding Data Extraction
- Managing Raw and Processed Data
- Wrangling Data using Python
- Central Tendency
- Central Tendency
- Probability Theory
- Data Acquisition
- Data Inspection
- Data Cleaning
- Data Transformation
- Data Integration
- Data Validation and Quality
- Documentation and reporting
Central Tendency
In the “Central Tendency” module, you’ll learn about Mean, Mode, and Median, including their definitions, calculations, and practical applications to analyse data effectively.
Central Tendency
This module covers data dispersion, including essential subtopics for deeper understanding.
- Variance
- Standard Deviation
Probability Theory
In this section, you’ll learn about Probability Density and Mass Functions, Statistics, Data Pre-Processing, Conditional Probability, EDA, and working with tools like Numpy, Scipy, Pandas, and Scikit-learn at FITA Academy.
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Type of Statistics
Central Tendency
In this module, you’ll learn about Mean, Mode, Median, Standard Deviation, Variation, Range, and Frequency Tables. These concepts are vital to the Syllabus Artificial Intelligence at FITA Academy.
Inferential
You’ll learn about vital statistical methods, such as the Probability Theorem, Distributions, P-value, T-test, Chi-square, ANOVA, and Null Hypothesis, which are essential for concluding data.
Data Pre-Processing
In this module, you will learn how to prepare data for algorithms and comprehend it mathematically. You will also explore techniques for cleaning and organising data for practical analysis and modelling.
Python Packages & Libraries
In this section, you’ll learn how to use essential tools like Statsmodels, Numpy, Scipy, Pandas, Matplotlib, Seaborn, and Beautiful Soup to enhance your data analysis skills.
- Statsmodel
- Numpy
- Scipy
- Pandas
- Matplotlib
- Seaborn
- Beautiful Soup
Data Exploration and Data Manipulation
Exploration
In this section, you will learn exploratory data analysis techniques, such as variables, univariate and bivariate analysis, addressing missing values and outliers, and the necessary data transformations and creations.
Manipulation
The “Manipulation” module teaches the fundamental techniques for filtering, cleaning, and reshaping data. At FITA Academy, you can learn to aggregate, index, slice, and pivot the data frames for effective data handling.
Understanding Machine Learning Models
In the “Understanding Machine Learning Models” module, you’ll learn a machine learning model, explore various types, choose the right one, and discover how to train, evaluate, and improve model performance.
More on Models
This module will discuss predictive modelling, including linear and polynomial regression, leading to multi-level models.
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Understanding Machine Learning Algorithms
In this module, you’ll learn about Machine Learning algorithms and their importance and explore different types, such as Supervised, Unsupervised, and Reinforcement Learning. Discover key concepts in the Artificial Intelligence Subject Syllabus.
Exploring Supervised Learning Algorithms
This module will teach you some essential supervised learning algorithms that any data analyst must know. Learn about Logistic Regression, naive Bayes, SVM, Decision Trees, and more to enhance your skills and become a better data analyst.
Time Series Analysis (TSA)
Learn about TSA, its terms, advantages, key players, AR and MA models, stationarity, and how to carry out forecasting effectively with TSA techniques.
Exploring Un-Supervised Learning Algorithms
You’ll learn about unsupervised learning, including clustering techniques like K-means and Hierarchical Clustering, and dimensionality reduction methods.
Exploring Hierarchical Clustering
In this section, you’ll learn about hierarchical clustering techniques, including understanding their principles and implementing them through practical exercises to analyse and group data effectively.
Understanding Dimensionality Reduction
In this module, you’ll learn about the importance of dimensions, the purpose of advantage dimensionality reduction, and critical techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
Understanding Hypothesis Testing
you’ll learn about hypothesis testing in machine learning, its advantages, basics, normalisation, and standard normalisation. Gain insights into key concepts with the Artificial Intelligence Syllabus at FITA Academy.
Parameters of Hypothesis Testing
In the “Parameters of Hypothesis Testing” module, you’ll learn about the Null Hypothesis, Alternative Hypothesis, and their key parameters. Also, you’ll learn about key concepts like P-Value, T-Test, Z Test, ANOVA Test, and Chi-Square Test, including their types and applications, at FITA Academy. For more insights, check out Artificial Intelligence Interview Questions and Answers to boost your preparation!
Overview Reinforcement Learning Algorithm
In this module, you will be introduced to basic reinforcement learning, its benefits, crucial elements, and the tradeoff between exploration and exploitation-what you would want to know to master that AI technique.
Hands on Projects
Throughout the module, you’ll develop practical skills through real-world projects. You’ll apply concepts, solve problems, and build solutions to enhance your expertise. AI Engineer Salary For Freshers varies based on location, company, and skill level but is generally competitive due to high demand. As AI evolves, it brings new career opportunities and continuous advancements, offering freshers a dynamic and rewarding career path.
Deep Learning
Introduction to Deep Learning
This module teaches the basics of reinforcement learning algorithms by working on practical projects. Learn fundamental AI Course Syllabus concepts along with real-world applications.
Basics of Neural Networks
The course will help understand basic concepts such as binary classification, logistic regression, and gradient descent. You go further in derivatives, computation graphs, and vectorization; you also learn how to optimise logistic regression with real examples.
Shallow Neural Networks
In the “Shallow Neural Networks” module, you’ll learn the principles of neural networks, such as their architecture, output computation, and the role of activation functions, derivatives, and gradient descent for effective learning.
Deep Neural Networks
In this section, you’ll learn about constructing L-layer networks, forward and backward propagation, and the role of deep representations. Investigate dimensions of the matrix, parameters vs hyper-parameters, and inspiration from the brain.
Deep Learning – Computer Vision
Introduction and Overview
In the course, you will learn the basics of image processing: course overview, how images are formed captured, linear filtering, correlation, and convolution in order to be better equipped for analysis of visual data.
Visual Features and Representations
In this section, you’ll learn about edge, blob, corner detection, scale space, and critical algorithms like SIFT and SURF. Discover HoG and LBP techniques for practical image analysis, which is essential for the AI Syllabus.
Visual Matching
You’ll learn about Bag-of-Words, VLAD, RANSAC, Hough Transform, Pyramid Matching, and Optical Flow. This part of the AI Syllabus focuses on the basic techniques to recognise an image.
Review of Deep Learning
This module will introduce you to Multi-layer Perceptrons and Backpropagation. Learn these basics to learn more about deep learning techniques.
Convolutional Neural Networks (CNNs)
In this section, you’ll learn about the basics of CNNs, their evolution, and key architectures like AlexNet, ZFNet, VGG, InceptionNets, ResNets, and DenseNets.
Visualisation and Understanding CNNs
You’ll learn to visualise kernels, explore deconvolution methods, and understand techniques like Deep Dream and Grad-CAM, along with recent methods for improved image analysis.
CNNs for Recognition, Verification, Detection, Segmentation
In this module, you’ll learn about CNNs for various tasks like recognition, verification, and detection. Explore Siamese Networks, R-CNN variants, YOLO, SSD, and RetinaNet for advanced object detection techniques as referenced in popular Artificial Intelligence Books.
CNNs for Segmentation
You will be introduced to more complex architectures, such as FCN, SegNet, U-Net, and Mask-RCNNs, along with their key features and applications for effective image segmentation.
Recurrent Neural Networks (RNNs)
In this module, you will learn about basic RNN, coupling the RNNs with the CNNs for video analysis, spatiotemporal models, and action and activity recognition techniques that have made possible complex tasks easy.
Attention Models
You will study cutting-edge ideas like Vision and Language, Image Captioning, Visual QA, Visual Dialogue, Spatial Transformers, and Transformer Networks in the “Attention Models” part of our Artificial Intelligence course.
Deep Generative Models
You will learn about the well-known models, including GANs and VAEs, as well as other innovative models such as PixelRNNs, NADE, and Normalizing Flows. This will add a better understanding of generative techniques into your knowledge.
Alternative and Applications of Generative Models in Vision
Applications
In this module, you’ll learn about cutting-edge techniques like Image Editing, Inpainting, Superresolution, 3D Object Generation, and Security. Discover how these innovations are applied in real-world scenarios at FITA Academy.
Variants
Students will learn about CycleGANs for unpaired image translation, Progressive GANs for high-quality images, StackGANs for text-to-image synthesis, and Pix2Pix for paired image translation.
Recent Trends
In this module, you’ll learn about Zero-shot, One-shot, Few-shot Learning, Self-supervised Learning, Reinforcement Learning in Vision and Other Recent Topics and Applications.
Deep Learning Natural Language Processing
Introduction to Natural Language Processing (NLP)
In this module, you will learn about the definition, scope, importance, applications of NLP, and the changes it experienced in the past few decades. This will create a great foundation in this exciting field.
Basics of Linguistics for NLP
In the “Basics of Linguistics for NLP” module, you’ll learn key concepts in Phonetics and Phonology, Morphology and Syntax, and Semantics and Pragmatics, vital for mastering the AI Course Syllabus at FITA Academy.
Text Processing and Preprocessing
In this section, you’ll learn essential techniques for preparing text for analysis, such as Tokenization, Stop Word Removal, Stemming and Lemmatization, Part-of-Speech Tagging, and Named Entity Recognition (NER).
Language Modeling and Text Representation
In this module, you’ll learn Bag-of-Words, TF-IDF, Word Embeddings (Word2Vec, GloVe), and Contextualized Embeddings (BERT, GPT). These techniques are key parts of the Artificial Intelligence syllabus at FITA Academy.
NLP Techniques and Algorithms
In this module, you’ll learn essential NLP techniques and algorithms, including machine learning methods like Naive Bayes, SVM, and Decision Trees, and advanced deep learning models such as RNN, LSTM, CNN, and Transformers.
Sentiment Analysis and Opinion Mining
In this module, you’ll learn Understanding Sentiment Analysis, Feature Extraction for Sentiment Analysis, and Building Sentiment Analysis Models, gaining essential skills to effectively analyse opinions and emotions from text.
NLP Tools and Libraries
In this module, you’ll learn about key NLP tools like NLTK, SpaCy, and Gensim and use TensorFlow and PyTorch for NLP tasks. These tools help process and analyse human language efficiently.
Case Studies and Practical Applications
In this section, you’ll learn about real-world NLP projects, challenges and solutions in NLP applications, and explore future trends in NLP. These topics are covered in the AI Syllabus at FITA Academy.
